The proposed experiments will investigate human observers' ability to detect and locate lesions on CT images. Lesions of varying sizes will be superimposed at random locations, either within designated areas on CT images of water phantoms or within the area of a patient's liver on clinical CT images. The experiments will consider how detection performance is affected by such factors as the lesion's size and contrast, the level of pixel noise on the CT image, the image reconstruction algorithm, the visual-display settings and the size of the area searched on the CT image. Quantitative measures of observers' detection performance, and their judgments about particular locations on the CT images, will be compared to: a) calculations of the lesion signal-to-noise ration and b) specific predictions derived from a proposed model of the human observer. This model predicts the observers' performance from calculations performed on the CT image information, using a series of lesion-matched filters that are scanned over the designated areas of search on the phantom or clinical CT images. The analysis of observer performance will generate ROC curves from the observers' confidence ratings about the presence of focal lesions, either within designated areas or at specified lesion and non-lesion locations on the CT images. The fitted ROC curves provide quantitative indices of the ability to distinguish the areas (or specified locations) that contain focal lesions from those that do not, independent of any changes in the observer's detection criterion. These measured indices of lesion detectability can be studied as functions of: a) individual physical variables (e.g., lesion size or contrast) and b) calculations on the image information (e.g., lesion signal-to-noise ration or model predictions). The fitted ROC curves also provide a way to scale and average individual ratings of specified locations, so that observers' "consensus" judgments about particular lesion and non-lesion locations on the CT images can be compared to the calculations from lesion-matched filters. The results will improve our understanding of the perceptual processes that underlie the detection of lesions on CT images, and will help to develop a theoretical model that can predict the human observer's performance.